{"title":"Hyperspectral Image Classification via Cascaded Spatial Cross-Attention Network","authors":"Bo Zhang;Yaxiong Chen;Shengwu Xiong;Xiaoqiang Lu","doi":"10.1109/TIP.2025.3533205","DOIUrl":null,"url":null,"abstract":"In hyperspectral images (HSIs), different land cover (LC) classes have distinct reflective characteristics at various wavelengths. Therefore, relying on only a few bands to distinguish all LC classes often leads to information loss, resulting in poor average accuracy. To address this problem, we propose a method called Cascaded Spatial Cross-Attention Network (CSCANet) for HSI classification. We design a cascaded spatial cross-attention module, which first performs cross-attention on local and global features in the spatial context, then uses a group cascade structure to sequentially propagate important spatial regions within the different channels, and finally obtains joint attention features to improve the robustness of the network. Moreover, we also design a two-branch feature separation structure based on spatial-spectral features to separate different LC Tokens as much as possible, thereby improving the distinguishability of different LC classes. Extensive experiments demonstrate that our method achieves excellent performance in enhancing classification accuracy and robustness. The source code can be obtained from <uri>https://github.com/WUTCM-Lab/CSCANet</uri>.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"899-913"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10857952/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In hyperspectral images (HSIs), different land cover (LC) classes have distinct reflective characteristics at various wavelengths. Therefore, relying on only a few bands to distinguish all LC classes often leads to information loss, resulting in poor average accuracy. To address this problem, we propose a method called Cascaded Spatial Cross-Attention Network (CSCANet) for HSI classification. We design a cascaded spatial cross-attention module, which first performs cross-attention on local and global features in the spatial context, then uses a group cascade structure to sequentially propagate important spatial regions within the different channels, and finally obtains joint attention features to improve the robustness of the network. Moreover, we also design a two-branch feature separation structure based on spatial-spectral features to separate different LC Tokens as much as possible, thereby improving the distinguishability of different LC classes. Extensive experiments demonstrate that our method achieves excellent performance in enhancing classification accuracy and robustness. The source code can be obtained from https://github.com/WUTCM-Lab/CSCANet.